Friday, December 01, 2006

JSpecView Demo

Original Post

Jean-Claude Bradley: So I’d been meaning for a little while to do a screen cast to show why it is so useful to use JCAMP, and specifically JSpecView, to look at your NMR spectrum. Here’s an example on Experiment 42, which is an experiment that was done recently by MI grad student, Khalid. This is on the UsefulChem wiki. And it involves basically mixing phenylacetaldehyde with t-butylamine, trying to monitor the formation of the resulting imine. Now, what makes using JCAMP so useful is that you don’t have to take a whole bunch of expansions to get the information that you need from the spectrum.

Let’s take a look at this phenylacetaldehyde, for example. We basically just have a single file on HTML, and when we click on it you see that the spectrum pops up in a little applet, and we can do a lot of things to this without needing to look at different printouts. For example, if I look at the aldehyde peak, I simply have to left click, drag, and where I want to stop I can see that this expansion happens.

So I can expand the spectrum all the way to see that triplet very clearly and be able to take differences between the peak positions to get JCONSTANTS [sp] for example.

Now, if we go back to the original spectrum, we’re going to clear all the views, we can also do integration. So to do the integration on JSpecView we right click, we view and then integrate, and we can modify these factors to make it look prettier, but let’s just use the defaults. And we see this yellow integration line. Now again, we can extend any area that we want. So if we looked at the CH2 [sp] group for example, this is phenylacetaldehyde that we’re looking at, we can expand it, we can see that the integration can be calculated pretty easily. On the top right corner there are two numbers that are giving you your position at all times with the cursor. So if we look at the top of the integration we’re looking at roughly 125, right? And underneath is 105. So we’re looking at about 20 units for two hydrogens, OK?

So now we will zoom back, clear all the views, and we’re going to expand the aromatic region. And again we can subtract the two numbers, so disintegration ends at about 105 and starts at about 55. So we have 50 units for five hydrogens. Again, we have about 10 units for hydrogen.

We’ll zone back, clear all the views, and look at the aldehyde area. So now this one has about 54 minus 48, so we’re looking at six units is supposedly representing one hydrogen. So there is a problem with the integration on this spectrum and I am putting this up as a question out there to try to see if anybody can actually answer me as to why we’re having an integration discrepancy with this spectrum.

So this brings up another really useful aspect of JCAMP in that all of the parameters of the NMR experiment are automatically recorded. So in JSpecView, to see that, we right click and then we look at view, show header, and we can see all the parameters here. So we can see that it was done on a 300 MHz machine. We can look at any number of scans and just everything that was done for these experiments.

So now we can monitor what happens when we mix phenylacetaldehyde with.

T-butylamine, so we’ll go back. And there are a number of spectra here. If you look at the logbook, most of them have already been linked, but we’ll just take a look at the very first five minutes after mixing.

So again, we click on this link, and we have in JCAMP format, so let’s take a look at some of the new peaks that are coming out after five minutes of mixing the aldehyde and the imine. Well, we still have our aldehyde, our original aldehyde triplets. Zoom back out. And we can see in this aromatic region, a little bit down still from there, we have a few more peaks that are popping up, and I want to focus specifically on this little triplet here that’s coming up.

So if I magnify this, I can see that it’s coming in at about 7.65, and I can measure the coupling constants, which is 5.3 Hz in this case. I can then zoom out, all right? Because I’m thinking that that triplets may be the imine proton that will be coupling with the ECH2 [sp] group. So if we look elsewhere in the spectrum, a little bit more up field, we see that we have another little doublet that’s popping up, and this doublet is around 3.57. And again, if we calculate the JCONSTANT [sp] for this doublet it’s 5.3 Hz, and so there is a good likelihood that those two peaks are representing our imine that we’re trying to form.

Let’s click back to take a look at the molecule that we’re talking about. So we’re talking about the imine of phenylacetaldehyde and t-butylamine. So it turns out that this reaction actually does not end up being clean. If we take a look at what happens after 42 minutes we see that there is in fact a lot of stuff coming in besides the imine, especially if you look if we look at this region between two and six roughly. There are all kinds of other peaks coming in. Maybe these are related to some elbow [sp] condensations that are happening, but the bottomline is that this reaction is really difficult to do directly like this.

So hopefully I’ve demonstrated with this little example just how useful it is to keep your spectra in JCAMP format, and I’ve got to have to thank Robert Lancashire for developing JSpecView to enable us to do this. These spectra were very easily converted from their original format on our Varian 300 MHz instrument, and we actually have a little tutorial on how to do that. If you go to the UsefulChem site, click on the contents page, you’ll see instructions here for saving in JCAMP format on the 300 MHz machine, for example, and that should enable you to do the same thing so you don’t have to spend a lot of time printing out expansions for regions that you’re not sure that you want and need. Just post your file online and let people at it.

UsefulChem Drexel MiniSymp

UsefulChem Drexel MiniSymp

Original Post

Jean-Claude Bradley: All right! I want to talk about 15 minutes, so I'd like to give you an idea about the kind of things that are going on in my lab right now.

As you'll see all of what you see on this PowerPoint is actually available online, so you can click through and read a lot more in detail what's going on. First thing we are doing is that we are doing open-source chemical research. If you have been paying attention to C&E news, there was an article in July you can see here. This is me, and there is Khalid, and James. And this is actually methylene blue, which has nothing to do with our project. The photographer wanted something colorful. But this is actually something I think is very exciting. Which involves sharing all your experimental results and your thoughts with the Open Community as you get it. I'll show you how we can do that.

So, everybody has a different motivation with respect to Open Source Science. Mine is that I think we're heading towards a world where instead of it being humans collaborating with humans, it's going to be machines interacting with machines. And on the way to that, I think that we're going to have to get comfortable with humans interacting with machines. I don't think that we're there at this point, especially in chemistry. I think that we're on the way and chemistry is lagging behind bio - molecular biology. This, for example, is a robot scientist. This is a Ross King's invention and it actually will generate hypotheses, design experiments, execute the experiment and then re-design an experiment based on the results. Now this is what's happening in molecular biology, and there is no reason that the same thing cannot happen in chemistry. It's just that it is more conservative and there are different ways of doing things. But I can show you how we can start to approach that. I haven't got a lot of time to go into all the details of this - the bottom line is that I think that this is going to happen by a bottom up process, as opposed to a top down where somebody decides how things are going to happen. I think that it's going to happen because right now we live in an era where you can generate automated agents that can participate with the world with zero, or near zero, cost. And all the services that I'll be showing you today are free and hosted for you. So anyone in the world can do this if they have the desire.

So the first part of this is, you know if you wanted to interact with machines. How will they know what to do. I think that this is one of the hardest problems to solve. And the answer is "ask the humans." And so a year ago, I started this UsefulChem project by asking a simple question. Submitting these search terms "what is needed now," "a pressing need" - various terms - in articles that appeared in 2005 to see what it is that humans were saying is important to do in chemistry. And a number of things came up. One of the things that really impressed me was this: there is a pressing need for identifying new drug-based anti-malarial therapies. That's a theme that was recurrent and that's actually something that I hit across with people who are doing Open Source Science, in general. The other thing that happened later in the summer last year is, I found this site called Find a Drug. This is a non-profit that looked at enzymes of various diseases and had tens of thousands of people do computations on 500 million theoretical molecules to see if they could fit. So they used this kind of distributed processing approach. I contacted them and they sent me their library of 220 compounds that are predicted to be enoyl reductase inhibitors for malaria. And this is what they look like. They are diketopiperazines, and the interesting thing about this is that the whole point of this project is to do everything out in the open and so the very design of the synthesis was done in a blog and you can go back and see how those ideas developed. Initially, I was going to do a solid support synthesis and realized the limitations of that and then I came across this very simple Ugi synthesis followed by cyclization that's pretty general and it because all the compounds were diketopiperazines.

So I'm going to be introducing various components as they evolved over the course of the year. One of the first tings that we did is setup a Molecules blog, which is basically just a normal blog on Blogger - free and hosted - where you actually can put a SMILES code of the molecule that you are interested in. So this can be a molecule that we want to make. It can be a molecule that we need to purchase. It can be intermediate. Basically, any molecule that has anything to do with our research group is put in there automatically and it gets its UC number - UsefulChem number. And then what we did is we had an Experiments blog where we've linked from the blog to the molecules blog. So every time we used adrenaline we linked to that entry for adrenaline in the molecules blog. One of the intermediates that we needed to make is DOPAL, it's this catecholaldehyde and actually you can't purchase it commercially, you have to make it. And so we looked into the literature, and that also is fully detailed. If you go back you can see how we developed that and it turns out that there is a way from adrenaline by heating it in acid that you can actually make this. I don't have enough time to go into the full detail but it is actually kind of interesting. One of the things that happened as we started this project, because this is all done live and because blogs, especially something like Blogger, is indexed very quickly by Google is that other people can find out what you are doing very, very quickly. And so we started to get these comments by other chemists. Matt Todd is a chemist at the University of Sydney and he was making comments about the concentration that we were doing in this reaction. The interesting thing about this is that the reaction is that the reaction had not even been finished and already we were getting comments on it. And that is really where I see the power of this kind of Open Source chemistry.

Now eventually, it turned out that doing things in a blog was kind of limited when you start to accumulate a lot of information. And so, I created this Wiki - which is just a website that anyone can modify very quickly - to organize what was happening in the various blogs that we were using. And so here I can detail the history of what I just told you and have links to the actual blog entries.

Now this is the really nice thing as far as I am concerned is that almost everything we've done so far has been failures, which is actually typical for a research lab. But because we are recording absolutely everything that we're doing we can actually use those failures to tell a story. If you go on the wiki, we eventually did successfully make DOPAL, but there were a lot of problems, there was some experimental data that was incorrect in the literature. We didn't know that until we figured it out finally, but you know that whole story is available for anyone to benefit from, normally that would never make it into a standard journal article. Now what we start to do is, as we're using the wiki we realized that actually a wiki is a better way to manage raw experimental data compared to a blog because you can do things like this, you can actually click on a page and get a history of who contributed what at what time. And you can actually revert to any of these versions, so if something bad happens, you can actually go back and nothing's lost. And you can actually see for each edit exactly what was done. In case of this example, I guess this is Khalid who actually ended up putting how many milimoles he had of the material. So the thing is never quite done - it's always in a process of flux. But there is always information available to anyone who wants it.

So, the really nice thing about the wiki is that it has a third party timestamp that the blog doesn't. That means that you can actually refer to a specific version. So if you claim that you've done something first, there is a third-party timestamp and a link you can give to somebody and say "I did that on this day", and exactly what it was. That's something I think is very very powerful as more people get involved with this.

You can do all kinds of things. If you go on our wiki you can click on "Recent Changes", you can look across all experiments, what everybody did, and you can follow up and see when NMR was done or what happened.

A lot of other interesting things: again remember, I'm using only free and open hosted systems here. There is a little site meter that you can put in on your wiki, or on your blogs, that will tell you how people are finding your site. And this is something that I check every day. It's very interesting to see how people are actually finding our experiments. For example, on this one, someone typed in Schmoogle, which is a chemical search engine. Somebody typed in chemistry or protease inhibitors. Then we're linked from other blogs so we can track are actually finding us. And this is a very important component of understanding how your research is being disseminated.

The other thing is on the Molecules blog, we use various representations for molecules. One of them is InChI. And that's something that is a new way of representing molecules that has the advantage that it gets indexed on Google in a way that is unique for each molecule and people can find it very quickly. Here, for an example, is a search of the InChi code that finds our useful Chem-molecules blog. Now, Dave Strumfels in our group is doing a lot of automation work and so because we have all these feeds available at all times, we can actually have automation happen to them. For example, the Molecules blog has at the very minimum a SMILES code. Every day there's a script that runs that actually takes that SMILE codes, calculates the InChi, figures out the molecular weight and then goes online to find potential suppliers and converts that into various feeds so this is one of them. So the advantage here is that you can fully systematize the way of doing research where someone actually finding the molecule may have no clue how to find the chemical supplier but they just dump it in there. You can see how we can start to do automation. I would like to talk more about automation, but I don't have much time. There is something called CML or Assess, which is very, very new, a way of representing chemical information in a format that's blog-like, but it retains the chemical information - it's not just a picture.

So, this has been a very interesting project. What we've found by doing this is that we are automatically connected with some other Open Science people out there - the Synaptic Leap. A lot of these people are involved in doing diseases that don't have a lot of commercial interest and malaria is one of them. Most people who are sick can't afford the expensive therapy.

Another really exciting thing is again because we are making this fully open; we can start to collaborate with people who are not even in science. Here is a collaboration with the Lehigh Carbon Community College with Beth Ritter-Guth's students. She has English students and she has technical writing students that are actually going on our wikis and our blogs and are writing about how that work connects with what people would want to know about Malaria that don't understand chemistry. So her students are interviewing my students and trying to understand what we're doing in chemistry. They are also putting stuff up on wikis and blogs. So everything is being shared in real time with everyone. This is really the power. Remember if we were to wait to have enough information to publish in a regular journal none of this would ever have been made public.

So our next steps, basically we want to continue to extend our automation. There is a website called eMolecules that is basically catalogs molecules. They have about five million molecules written down in their database. We've just submitted our molecules to the database so automatically our molecules will end up in the public database. The nice thing about that is that is that if you do a substructure search, they will find our compounds.

And the other thing is we are moving our spectra to JCAMP format, so that for example, if you put in an MR, instead of having a picture, you can actually expand the range, expand the peaks and perhaps even redo the integration. And so ultimately though we want to make these anti-malaria compounds and we want to have them tested. A number of students are working on this project, Khalid and Alicia, both grad students doing experimental work.

Dave Strumfels is doing the Cheminformatics component. Remember, Cheminformatics is very different from Bioinformatics- don't get them confused. Bioinformatics has been around for a while. It has its own standards that apply because the information is very structured. In organic chemistry, it's a little bit different and you need another system. And, a couple of undergrads- James, Lin and Brett a while ago. And also I'd like to thank all the bloggers that contributed to our work; contributed code and contributed ideas. And we definitely will continue that, so if that's of any interest, come talk to me.


Transcription by CastingWords

ACS UsefulChem talk (podcast)

ACS UsefulChem talk (podcast)

Original Post

Man 1: Are you guys hearing?

All right. I'd like to talk to you today about the UsefulChem project, and this basically involves open source chemical research using blogs and wikis.

So from a larger perspective, just to try to give you an idea of why we're trying to do this research and where it comes from, you take a look at scientific research today. It's mainly human-to-human interaction, human-to-human communication. That's the way it's been since I started.

Well, we're entering a very interesting period right now where humans are starting to collaborate with machines. You saw that in this symposium, using machines to try to process information and to try to contribute to scientific information. I don't think we are actually there yet, that's why the arrow is right here. I think that we are moving towards that where machines and blogs will actually be real collaborators and contributors. Eventually I do see that humans are going to be bottleneck in scientific process and are going to be out of it, and it's going to be machines and machine interaction. So that's where we're headed with this, and let's see what it is that we can do today in that direction.

There are people who are definitely trying to make this happen from different fronts. Here's the robot scientist -- this is Ross D. King's project. His robot, apparently, can formulate hypotheses and can execute experiments and can analyze them. Now, the thing is that he is doing this on the yeast genome, so his equipment is going to be pretty limited. I mean basically, he is looking for expression, and so all he's doing is growing yeast. You can't really do the same thing for chemistry, because the tools that we use are so varied compared to the biological world. But I think this is a good example where things are headed.

How do I think this is going to happen? Well, I think that if we have self-organizing redundant processes... in other words, instead of having these top-down approaches where you design a large system, and then you have that system write stuff to a database and then you understand exactly how that's getting delivered, I think that what we're observing today... you know, you saw a lot of people use different standards, for example, to represent molecules. That's just the way that people are. They are going to do things a certain way, and it's going to be very difficult to get everybody to do things the same way. So I think that's a good thing; I think that these processes will, in fact, self-organize, and that's actually beneficial.

One of the things that's happening right now is that it's possible to participate with zero or near-zero cost. So the tool that I'll be using, most of them, involve using free and hosted services. This is the kind of thing that anyone can actually do to contribute to science. Also, we're entering a world of fully-open access, both read and write. You've heard about open access. Typically the way the term is used now, today, is that it's free to read articles, but a lot of these journals actually charge significant amounts of money to the author. So it's not open access from the standpoint of the author.

But there are -- for example, the Beilstein Journal of Organic Chemistry, ArticBot, those are fully open to read and to write, and of course blogs and wikis, which you'll see why it is that we're using it.

The kind of research that I'll be talking to you about is open-source science, and by that I mean that everything is exposed. We expose the raw data, we expose the thinking behind the experiments that we do, and everything. I'll be giving you very small milestones through this process, but if you're interested, this is all recorded and you can certainly go through it.

OK, so if we want machines eventually to do research, how will they know what to do? I think they need to ask the humans. And the way that we do that is by looking at what humans are saying in their papers as to what is important. So this part started about a year ago and it's just a very simplistic approach of looking at these search terms, such as "what is missing is," "what is needed now," and looking in journals to find out what people are saying that is really important to do. In 2005, a couple of things came up. There's a pressing need for the identification of newer, more effective dyes. In our region, some fuel cells, and then this one: there's a pressing need for identifying and developing new drug-based anti-viral therapies. So these were being collected, and as this was happening, came across this site called FindADrug. This is a not-for-profit organization that has screened hundreds of millions of molecules against targets for various diseases. And one of the targets that they actually did was the enoyl reductase enzyme for the malaria parasite. I contacted them, and they sent me a library of about 220 molecules that they predict should be active against that enzyme. So that gave us a place to start, to actually try to do this open-source research.

The molecules were basically all diketopiperazines in this library. So they're all the same except they have different groups of R1, R2, and R3, and the first synthesis that I proposed was a solid-support synthesis. You end up getting diketopiperazines often when you do peptide synthesis, and so that was one of the approaches. So I was writing that to the blog, and you can see the evolution of my thinking on this. But eventually I stumbled across this Ugi reaction, cyclization, which is one-pot. You basically bring an aldehyde, an amine, a carboxylic acid, and an isonitrile together, mix them up at room temperature and then add acid, and you get this diketopiperazines. So that's a pretty efficient synthesis, and that's the one that we selected.

To do this research on wikis and blogs, I will show you the various components and how that actually evolved over time. The first thing that we set up was this molecules blog, where we have the SMILES code and then we have the picture. As students are contributing to this project, if they find information that is relevant, the thing we care most about is commercial availability and how much it costs. So that's the kind of thing that students might put in here. And if we get any hard data we'll put this in, and basically this is in a blog, you can subscribe to an RSS feed and you can find out when new things got added or changed. We created another blog, which is the experiments blog, and this one -- you see these links, these actually link back to the molecules blog. So basically, if you're reading this information and you want to find out more about adrenalin, you click on it, and it'll take you, you'll see the NMR spectra, you'll see everything that you need to be able to understand what we did.

Now this is interesting, because this is done completely in the open, and blogs are pretty well indexed by Google and various search engines. So we started to get comments -- like here's a Matt Todd, from the University of Sydney, made a comment on our experiment three saying that we should probably be using a higher concentration. That's exactly the kind of thing that we want to encourage. This experiment was not even finished yet and we were already getting feedback.

Now, as you start to accumulate these experiments, you find that a blog isn't very good for organizing, so we created a wiki, the Useful Chem Wiki, and here, basically, all these links, these are linking to the various blog posts. But it's a place where someone who wants to be briefed very quickly can just read through that and can click. So we use the wiki for the organizational aspects of it and we use the blog because it has a nice feed.

A nice thing about this is that the vast majority of our experiments are failures, which you normally don't get to see. But in this case, from the wiki, if you go on this page you will see the actual story of the failures. You'll see why it is that we failed - there was some information in a peer-reviewed journal that happened to be incorrect. We were following that, and that whole thing is described. That is something that you would not normally get from a traditional journal article. Now eventually we found that even trying to maintain the blog was a little bit difficult because the students were doing their experiments and constantly updating it. You couldn't really tell how this blog post ended up the way it was.

So we started to use a wiki to actually store the experiments as well. It looks kind of similar to the blog entry except that you can do this: you can look at the experiment's history. So for this experiment 25 I can see exactly when and who modified it. I can click on any of these versions to find out exactly what happened. I can see a comment was made and then it was responded to, or a spectrum was put up.

So here's an example. In the red here, this is what was deleted, and in the green, this is what was added. So I had put why milimhos are such a percent yield because the student had not calculated it and I wanted them to do it. And then this is actually Colleen, my graduate student, came in and actually put in the values. So that's how it works. And you can actually tell when and how all this was done.

Now the really nice thing about using a wiki for your experiments is that you have an unambiguous, third-party timestamp as to when the experiment was done. If we ever go down the road and somebody claims that they did this experiment, we can go back and see what was the timestamp, and we can actually make a reference to that specific version. So not just the wiki entry, but also the actual version. I think that's going to become very important in terms of precedents.

You can also monitor the entire wiki. So you can see these are all the various experiments, different times, and who made the last entry. Now the other thing that I've put on my sites -- and everything I've shown you so far is 100% free and hosted, so it requires absolutely nothing to set this up. SiteMeter is also something that is also free and hosted. They will actually tell you how people are finding your site, so what keywords they're using. For example, somebody searched on Chmoogle, which is the former name of eMolecules, and they ended up on the site. There are also blogs that link to our wiki, so there are all kinds of ways that people are finding it. We also, for our molecules, put Inchi code, and so if you search Google using Inchi, you will in fact find our entries there.

So there's this whole other automation aspect here that I think this particular group might find interesting. You remember the blog that has all of the entries -- one post is one particular molecule. Well, the minimum amount of information for a post is the SMILES code, simply because it's the most useful string to have if you are searching databases. What this code actually does is to read the blog once a day and it will separate out the molecules or read the SMILES code and then it will return back the Inchi, the molecular weight, it will also hit e-molecules and this is in the Chmoogle URL, and it will tell you if there are commercial sources for that molecule. So this is useful because that's stuff that my students would have to do; as we do more and more automation, we're not going to have to do as much work to try to figure out stuff. And we also get the Jmole here connected.

Now we also, in addition to that series of web pages with the dropdown, we have a CML RSS feed that is viewable on Bioclips. So this Bioclips, you've seen a couple of times in this workshop, and this will be very useful in the future.

You can also read the CML RSS feed with a regular reader like Bloglines. It will ignore the CML part, but it will in fact display your molecule and your Inchis and all that stuff. And these readers are already built to tell you about new stuff, which as far as I know Bioclips isn't yet capable of doing.

OK, let's skip through a couple of things here.

As far as I'm concerned, it's a really exciting world that we're entering. There is not a top-down approach here. This is a system that evolved over time, and as we needed things, we added to it, and we were just transparent about what we were doing in our lab. What's been interesting about this is that we've interacted with other groups that are out there that are also interested in doing open-source science. The Synaptic Leap is another organization that wants to coordinate open-source science for tropical diseases mainly, so malaria definitely falls under their interest as well. ChemRefer is a very small operation that they look at our RSS feed, and if they could find articles that can help us, they'll send it to us. And they have, in fact, helped us at one key point in the design of our synthesis.

The last connection here is that doing all this out in the open also enables other people to collaborate with us. Beth Ritter-Guth is I believe at Lehigh Carbon Community College, and she has a couple of classes where her students, who are taking technical writing and English, are actually looking at our blog and trying to make sense out of it to explain to people who are not chemists. So what they do is they interview me, they interview my students, it's sort of a dialogue that happens to try to explain what it is that we do. One of the projects they have early in the term is to define open-source science. So if you have any interest in that, maybe take a look at their blogs. It's open and you can contribute and give them feedback.

So, the next steps. What we'd really like to see in the short term is, we are submitting our molecules to eMolecules, and that'll be useful because it'll be on their site and we'll be able to do substructure searching without having to host any software ourselves. Right now, you can't do a substructure search on our site; you have to know the exact molecule that you're looking for. And it also enabled people to find it easier to find our molecules.

We're also developing custom CMO RSS feeds. So if you wanted it to only alert you when there's new commercial sources for these compounds, we can set that up and people can subscribe to it.

We want to get our spectra in the JCAMP format. Right now our spectra are just printouts from the machine -- like you get an NMR and it's just scanned in. It's really annoying, because of course if you want to look at the J constants, you've got to ask the student, and if they haven't done that expansion, they're going to have to retake the spectrum. So with JCAMP, you should be able to expand the spectra and even reintegrate if it wasn't done properly. So that's something we definitely want to get going.

We would also like to extend our collaboration with other chemists, especially.

People who are interested in doing docking. For example, we have intermediates that look like that they may in fact fit into the enoyl reductase pocket but we don't do docking. We haven't done it and there are people who do that all the time So that would be interesting for them to take our feed and to tell us hey, this one is actually worth making. Some of them are easier to make than the final products that we're trying to make. But of course what we really want is to get our antimalarials so that we can get them tested.

We have one person in the medical school at Drexel who will do simple in vitro testing of our compounds against red blood cells to see if it inhibits malaria. And again, we want to do all that out in the open, not just the final results but the whole methodology as to how we did it what it means. So that's basically it.


Moderator: Any questions?

Man 2: [inaudible question]

Man 1: The question was about the reaction database. Actually I was talking to Peter about putting this in CMO reaction format so that it has the actual reaction in CMO form. I'm a big believer in redundancy. Instead of picking one best way to do it, right now we're using the wiki because it works, but absolutely if there's a database where we can put our stuff we will definitely want to do that. It doesn't stop us from doing anything else. So if you have something specific in mind, definitely let's talk.

Man 2: [inaudible]

Man 1: Let's talk, definitely. You have a question?

Man 3: [inaudible question]

Man 1: The question about the funding agencies. Well, we're looking for funding. This project started from scratch a year ago and so now I think we have some really good data to show that this is feasible, this is doable. NIH is very interested in having people publish Open Access and Open Source. It's really a question of getting the right team of people together - I think if we really do want to go for NIH, we're going to have to find someone who seriously wants to get involved in the testing, and if we do, then I think that makes a lot of sense.

Man 4: Are all the participants located at in your lab or are there people from other places?

Man 1: The question is about where are people located. Actually from my acknowledgement slide here, Khalid is my graduate student, he's in my lab; Jane Giamarco is an undergrad; Lynn Jamieson is another undergrad; Dave Strumpels is the guy who did all the chem informatic stuff and he's also in my lab. But then we have this loose association of other people like others and Peter. We've been contact through our blogs and there are a lot of people like that who are contributing. Especially when it comes to voting stuff, because that's something that you don't need to physically give people materials. I would say right now mainly the physical experiments it's definitely in our lab right now, and we'd like to change that, and again it's not necessarily to get people to post stuff in our wiki. This is a model that is zero cost. It can be replicated by anyone in the world for free. We're using WikiBases, which is free and hosted and is actually owned by Google. We're also using Blogger, which is also owned by Goggle, so these are very, very stable platforms. Actually what I'd like to see is somebody saying, hey I'd like to do this kind of thing to actually replicate it so that they can go off and do it.

Man 1: OK, thanks very much.

Transcription by CastingWords